Optimizing population simulations to accurately parallel empirical data for digital breeding
Authors: Burns, M. J., Della Coletta, R., Fernandes, S. B., Bohn, M. O., Lipka, A. E., Hirsch, C. N.
Date: 2025-06-24 · Version: 1
DOI: 10.1101/2025.06.18.660215 Category: Plant Biology
Model Organism: Zea mays
▶ AI Summary
The study evaluated whether integrating genetic architecture information from GWAS into simulations improves alignment between simulated and empirical trait data in maize hybrids derived from 333 recombinant inbred lines across multiple environments. Using ≥200 top GWAS hits as causal variants and reducing estimated marker effect sizes increased correlations (0.397‑0.915) and accurately reproduced variance components and genomic prediction performance. This provides a pipeline for realistic digital breeding simulations.
GWAS simulation maize genomic prediction genetic architecture